Quadcopter unmanned aerial vehicles (UAVs) systems are receiving remarkable attention from researchers due to their daily use in numerous applications, particularly at the current time where UAVs are playing a significant role in combating the COVID-19 pandemic. This paper is concerned with the problem of UAV navigation and control in the presence of uncertainty and external disturbances. It addresses this issue by proposing an improved adaptive sliding mode control (IASMC). Improved control law generates an adaptive switching gain achieving fast adaptation and robustness against both parameter uncertainties and external disturbances. Simultaneously, the control law allows for maintaining robust trajectory tracking with chattering attenuation. The performance of the proposed IASMC controller has been evaluated using a Matlab/Simulink platform. The obtained simulation results showed that the proposed IASMC control scheme as an inner loop controller is robust when dealing with uncertainties and can effectively track the desired trajectories while significantly attenuating the well-known chattering introduced by the SMC controller.

The quadcopter unmanned aerial vehicle (UAV) system is considered a good platform for control scheme design as it is highly nonlinear with coupled dynamics and an under-actuated system. Considering these challenges, this manuscript aimed to propose a control algorithm to control the quadcopter system in the presence of uncertainty and disturbance influences. The integral adaptive sliding mode control scheme has been proposed to control the system. The proposed control scheme is composed of the outer loop controller to control the position of the quadcopter, while the inner loop controls the attitude of the quadcopter. The proposed control law has three major terms, firstly the equivalent control which is developed based on the Lyapunov approach to handle most of the uncertainty and disturbance, secondly the adaptive switching gain, which is achieving fast adaptation against uncertainty, finally the switching function which has been approximated by a tangent hyperbolic function to reduce the unwanted chattering phenomena. The proposed control scheme and its performance have been investigated via a MATLAB/Simulink. The results prove that the implemented control scheme is robust even in the presence of uncertainties and disturbance and the quadcopter tracks the predefined trajectories with limited chattering influence.

Fouling build-up is one of the most challenging problems for heat exchangers in industry. The presence of fouling leads to a degradation of system efficiency, an increase in operating cost, and possibly, a harmful environmental impact. For this reason, fouling analysis has become an extremely important research subject in order to have a safe and efficient operation. The analysis is more difficult where phase change of fluids is involved during the heat transfer process, as in the case of boilers and condensers, which are critical units in industrial facilities. Due to the lack of a comprehensive review of fouling analysis for the case of multiphase heat exchangers, this paper examines available approaches and techniques used for fouling characterization, modeling, monitoring, and prediction in heat exchangers for both single-phase and multiphase heat exchangers with a focus on fouling in thermal desalination systems. It also gives an overview of heat exchanger condition monitoring solutions available in the market.

The integration of artificial intelligence in industry is crucial for realizing Industry 4.0; however, the lack of industrial datasets remains a significant challenge. While several generative AI methods have been proposed to create synthetic data, these approaches are often inefficient and require a large volume of training data to function effectively. In this study, we utilize a physics-based rendering procedure to generate a synthetic dataset of aeroengine blades. This dataset is then used to train a defect inspection model, thereby addressing data scarcity and enhancing defect detection accuracy in industrial applications. The dataset generation process begins with preparing Computer-Aided Design (CAD) models and material textures, then constructing a realistic inspection scene incorporating domain-randomized camera settings, lighting, and background elements. The generated data is assessed for effectiveness in both supervised and unsupervised defect detection tasks. Additionally, sim-to-real transferability is examined, demonstrating that models trained on the generated synthetic data can effectively detect and classify defects in real blade images.